• Lost in Translation - A repository of the leaked tools

• MS17-010 - Port of some of the exploits to Windows 10

The practice of keeping sensitive keys in a .env file is a fundamental rule of modern development. It is the first line of defense that every developer learns. However, the rise of AI coding agents has introduced a subtle complication: keys are shared in agent chats and the agent might still end up creating a permanent record of those secrets elsewhere.

Even when a developer is careful to use a .env file, the moment a key is mentioned in a chat or read by the agent to debug a connection, it is recorded. We found thousands of lines of session data tucked away in hidden folders. Within these logs, API keys and access tokens were sitting in plain text, completely unencrypted and accessible to anyone who knows where to look.

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Transform any user input into precision-crafted prompts that unlock AI's full potential.

Review code changes the way Sahaj usually asks for review: go deep on reuse, composition, codebase consistency, and anything that reads like slop.

日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。